Text Generation
Transformers
Safetensors
English
reasoning
distillation
chain-of-thought
qwen
qwen3.6
mixture-of-experts
Mixture of Experts
lora
unsloth
Instructions to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
- SGLang
How to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled", max_seq_length=2048, ) - Docker Model Runner
How to use lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled with Docker Model Runner:
docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
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